Kumpulan Artikel ChatGPT

Mengarahkan Kecerdasan Buatan agar menjawab dengan benar

Yogyakarta, Senin, 22 Mei 2023

Daftar Isi

1. In order to get the correct answer from an AI based chat, what are the basic rules?
2. Basically what you do first when you are asked a question?
3. As a statistical approach AI, you only use statistical approach without understanding contexts, right?
4. Explain deep learning techniques to generate responses.
5. I read an article that AI based NLP, most of the time miss the meaning of the word "NOT" used by humans.
6. How to mitigate these difficulties?
7. Do you understand the principle of AI based image generation from a text prompt?
8. I noticed when you made a mistake then user made correction, sometimes you made more mistakes.
9. Are you still using the training data of September 2021?
10. Why don't you use directly data from the internet?
11. When will you update your training data?
12. Right now, I am interested in comparing between ChatGPT, Microsoft Bing AI Copilot, and Google Bard AI.

Djoko Luknanto: Sebagai rekapitulasi dalam Bahasa Indonesia: bagaimana sebaiknya pengguna mengarahkan kecerdasan buatan berbasis input teks, agar diperoleh jawaban yang akurat. Jelaskan secara rinci dan mudah dimengerti.

ChatGPT: Untuk mengarahkan kecerdasan buatan (AI) berbasis input teks agar diperoleh jawaban yang akurat, berikut adalah beberapa langkah yang dapat diikuti:

1. Spesifikasikan pertanyaan Anda: Ungkapkan pertanyaan atau permintaan Anda secara jelas dan terperinci. Usahakan untuk menghindari pertanyaan atau pernyataan yang ambigu atau terlalu umum. Semakin spesifik pertanyaannya, semakin besar kemungkinan AI untuk memberikan jawaban yang lebih akurat.
2. Sediakan konteks yang cukup: Jelaskan konteks yang relevan seputar pertanyaan atau permasalahan yang Anda ajukan. Informasikan AI tentang faktor-faktor yang mungkin berpengaruh terhadap jawaban yang Anda harapkan. Konteks yang lebih banyak dapat membantu AI memahami konteks situasi dengan lebih baik dan menghasilkan jawaban yang lebih tepat.
3. Batasi ruang lingkup: Jika topik yang dibahas terlalu luas atau kompleks, coba batasi ruang lingkup pertanyaan Anda. Fokuskan pertanyaan pada aspek yang spesifik atau bagian yang ingin Anda jelajahi. Hal ini akan membantu AI untuk memberikan jawaban yang lebih terarah dan relevan.
4. Gunakan pertanyaan yang terstruktur: Jika memungkinkan, sampaikan pertanyaan Anda dalam bentuk yang terstruktur atau berurutan. Misalnya, pecah pertanyaan yang kompleks menjadi beberapa bagian yang lebih kecil. Pendekatan ini dapat membantu AI untuk memahami secara lebih baik dan memberikan jawaban yang sesuai dengan setiap bagian pertanyaan.
5. Berikan klarifikasi jika perlu: Jika AI memberikan jawaban yang tidak sepenuhnya sesuai dengan harapan Anda atau jika ada kekurangan informasi, jangan ragu untuk memberikan klarifikasi atau perbaikan. Jelaskan dengan lebih rinci atau berikan konteks tambahan untuk membantu AI memahami pertanyaan Anda dengan lebih baik.
6. Bersabarlah dan gunakan umpan balik: Kecerdasan buatan terus belajar dan diperbarui berdasarkan umpan balik dari pengguna. Jika Anda menemukan kesalahan atau ketidakakuratan dalam jawaban AI, berikan umpan balik yang konstruktif. Hal ini dapat membantu tim pengembang untuk meningkatkan kualitas dan akurasi jawaban AI di masa depan.

Meskipun langkah-langkah ini dapat membantu dalam mengarahkan AI, penting untuk diingat bahwa AI tetap memiliki batasan dan kemampuan tergantung pada data pelatihan dan model yang digunakan. Kritis dalam mengevaluasi dan memverifikasi informasi yang diberikan oleh AI juga tetap penting, dengan mengacu pada sumber-sumber terpercaya dan berkonsultasi dengan pakar di bidang terkait jika diperlukan.

Harapannya, dengan mengikuti langkah-langkah ini, Anda dapat meningkatkan kemungkinan mendapatkan jawaban yang lebih akurat dari sistem kecerdasan buatan berbasis teks seperti saya.

Djoko Luknanto: In order to get the correct answer from an AI based chat, what are the basic rules?

ChatGPT: Prompt: "Basic rules for obtaining correct answers from an AI-based chat"

Instructions:

1. Begin the conversation by greeting the AI chat and introducing yourself.
2. Clearly state your question or problem in a concise manner.
3. Avoid ambiguous or vague language. Be as specific as possible in your query.
4. Provide any relevant context or background information that can help the AI understand the question better.
5. If applicable, specify any preferences or constraints related to the answer you are seeking.
6. Be patient and give the AI sufficient time to process and generate a response.
7. If the AI's response is unclear or incomplete, politely request clarification or ask follow-up questions to obtain the desired information.
8. If the AI chat asks for additional details or clarification, promptly provide the requested information.
9. Evaluate the answer provided by the AI critically. Consider the accuracy, relevance, and comprehensiveness of the response.
10. If the answer is not satisfactory or if you require further assistance, politely express your concerns or ask for alternative suggestions.
11. Remember that AI-based chats are designed to assist but may not always provide perfect or definitive answers. Use your discretion and consult multiple sources if needed.

Example conversation:

User: Hello! My name is Sarah. I have a question about AI-based chat interactions. Can you help me understand the basic rules for obtaining correct answers?

AI: Hello, Sarah! I'd be happy to assist you. Please go ahead and ask your question, and I'll do my best to provide you with the information you need.

User: Thank you! Well, my question is: What are the basic rules for obtaining correct answers from an AI-based chat?

AI: Great question, Sarah! To ensure you receive accurate responses, there are a few rules you can follow. First, be clear and specific when stating your question or problem. The AI performs best when it has a precise understanding of what you're asking. Second, provide any relevant context or background information that can help the AI better comprehend your query. Additionally, it's important to be patient and allow the AI sufficient time to process and generate a response. If the AI's answer is unclear or incomplete, feel free to ask for clarification or request additional information. Finally, remember to evaluate the answer critically and consider seeking alternative sources if necessary.

User: That makes sense. Thank you for the helpful guidelines!

AI: You're welcome, Sarah! I'm glad I could assist you. If you have any more questions, feel free to ask.

Djoko Luknanto: Basically what you do first when you are asked a question?

ChatGPT: When I am asked a question, the first thing I do is analyze the input to understand the context and determine the specific information being requested. I try to identify the key elements and any relevant details provided in the question. This initial analysis helps me grasp the user's query and generate a well-informed response. Once I have a clear understanding of the question, I utilize my pre-trained knowledge and language processing capabilities to provide the most relevant and accurate information in my response.

Djoko Luknanto: As a statistical approach AI, you only use statistical approach without understand contexts, right?

ChatGPT: As an AI language model, I employ a combination of statistical approaches and deep learning techniques to generate responses. While my primary mechanism is statistical in nature, I have been trained on vast amounts of text data to understand and generate human-like text. This training enables me to comprehend context to a certain extent. By analyzing patterns, relationships, and co-occurrences in the training data, I can infer context and generate contextually relevant responses. However, it's important to note that my understanding of context is based on patterns observed in the data, and I do not possess true comprehension or consciousness like humans do. My responses are generated based on statistical probabilities and patterns learned during training.

Djoko Luknanto: Explain deep learning techniques to generate responses

ChatGPT: Deep learning techniques are a subset of machine learning algorithms that are specifically designed to process and learn from complex patterns and hierarchical representations of data. These techniques are used in AI systems, including language models like mine, to generate responses.

In the context of generating responses, deep learning models typically involve neural networks with multiple layers of interconnected nodes, also known as artificial neurons. These neural networks are trained on large datasets, including text data, to learn the statistical relationships and patterns within the data.

The training process involves two main steps: forward propagation and backpropagation. During forward propagation, input data is fed through the neural network, layer by layer, with each layer applying mathematical operations to transform the data. The final layer produces an output, which in the case of language models, is a generated response.

The generated response is then compared to a target or desired response from the training data. The error or difference between the generated response and the target response is calculated. This error is then backpropagated through the network, adjusting the weights and biases of the artificial neurons in each layer. This iterative process, known as backpropagation, allows the neural network to gradually optimize its parameters and learn to generate more accurate and contextually relevant responses.

By training on a diverse range of text data and optimizing the neural network's parameters through deep learning techniques, language models like mine can generate responses that exhibit a degree of understanding and contextuality, even though it is important to note that the understanding is based on patterns observed in the training data and not true comprehension.

Djoko Luknanto: I read an article that AI based NLP, most of the time miss the meaning of the word "NOT" use by human

ChatGPT: Yes, you are correct. AI-based natural language processing (NLP) models can sometimes struggle with understanding the meaning of negation, particularly when the word "not" is used. Negation adds complexity to language comprehension because it often reverses the meaning of a statement. While AI models like mine have made significant advancements in language understanding, accurately interpreting negation remains a challenging task.

One reason for this challenge is that language models typically learn through statistical patterns in the training data, and negation can introduce ambiguity that is difficult to capture purely from statistics. The meaning of "not" heavily depends on the specific context and the words it modifies. In some cases, the presence of "not" can completely change the interpretation of a sentence.

Although AI models can generate responses based on statistical patterns, they may struggle to grasp the nuanced implications of negation. As a result, there are instances where the model might overlook or misinterpret the negation, leading to incorrect or contradictory responses.

It's essential to be aware of this limitation and carefully consider the model's responses, especially when negation is involved. If the presence or absence of "not" significantly alters the intended meaning, it can be beneficial to explicitly clarify your question or statement to minimize potential misunderstandings.

Djoko Luknanto: How do mitigate these difficulties?

ChatGPT: To mitigate the difficulties in AI models' interpretation of negation, there are a few strategies that can be employed:

1. Clarify and rephrase: If the use of negation is crucial to your question or statement, consider rephrasing it in a way that reduces ambiguity. Explicitly stating the intended meaning or using alternative phrasing can help the AI model better understand your intention.
2. Provide additional context: Supplying more context can aid the AI model in comprehending the intended meaning. By offering relevant background information or specifying the context in which the negation is used, you increase the chances of obtaining a more accurate response.
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4. Highlight negation: Emphasize the negation explicitly to draw the AI model's attention to it. You can use capitalization, italics, or other formatting options to make the negated word or phrase stand out, helping the model recognize its significance.
5. Use alternative constructions: Instead of relying solely on negation, consider alternative constructions that convey the intended meaning more directly. This approach can help avoid potential confusion caused by the AI model's interpretation of negation.
6. Provide feedback: If you notice that the AI model misinterprets negation in its responses, you can provide feedback and clarification. Sharing the model's shortcomings with the developers can contribute to ongoing improvements and refinements.
7. Cross-reference with external sources: When dealing with critical or sensitive information, it is prudent to consult multiple sources to verify the accuracy and validity of the AI model's responses. External references can provide a more comprehensive understanding and counteract any potential misinterpretations.

Remember that while these strategies can help alleviate the difficulties, AI models might still exhibit limitations in handling negation accurately. Careful evaluation and critical thinking remain essential when interpreting the responses generated by AI-based NLP systems.

Djoko Luknanto: Do you understand the principal of AI based generated image from text prompt?

ChatGPT: Yes, I understand the principle of AI-based image generation from a text prompt. The process typically involves using a deep learning technique called generative adversarial networks (GANs) or similar architectures.

GANs consist of two main components: a generator and a discriminator. The generator's role is to generate images based on a given text prompt, while the discriminator's role is to assess the generated images and determine whether they are real or fake.

During the training process, the GAN is exposed to a large dataset of real images along with corresponding text descriptions. The generator learns to map the text prompts to visual features and generates images that aim to match the provided descriptions. The discriminator, in turn, learns to distinguish between the real images and the ones generated by the generator.

Through an iterative training process, the generator and discriminator work in opposition, continuously improving their performance. The generator tries to produce more realistic images that deceive the discriminator, while the discriminator becomes more skilled at distinguishing real images from generated ones.

Once the GAN is trained, it can generate images based on new text prompts that it has not seen during training. By inputting a text description, the generator generates an image that aims to correspond to that description.

It's important to note that while AI-based image generation has made significant progress, there are still limitations in terms of generating highly detailed or specific images. The generated images may sometimes lack realism, exhibit distortions, or not fully capture the intended meaning of the text prompt. Nonetheless, ongoing research and advancements continue to improve the capabilities of AI-generated image synthesis.

Djoko Luknanto: I noticed when you made a mistake then user made correction, sometimes you made more mistakes?

ChatGPT: I apologize if there have been instances where I made additional mistakes after a user correction. As an AI language model, I strive to provide accurate and helpful responses, but I'm not infallible, and errors can occur. When a user points out a mistake and provides a correction, it is intended to help me understand the error and generate a more accurate response. However, there are cases where the correction may not be clear or the context might be misinterpreted, leading to subsequent mistakes in the follow-up response.

To mitigate this issue, it is helpful for users to provide specific and clear corrections, along with any relevant context if necessary. This can aid in improving the accuracy of subsequent responses. Additionally, I continuously learn from user interactions, including feedback and corrections, to refine my understanding and enhance my performance over time.

Thank you for bringing this to my attention, and I apologize for any inconvenience caused. If you encounter any errors or have any concerns, please feel free to point them out, and I'll do my best to address them.

Djoko Luknanto: The problems sometimes due to limitation of your training data

ChatGPT: You are correct that limitations in training data can contribute to errors or inaccuracies in my responses. As an AI language model, my responses are generated based on patterns and information present in the data I was trained on. If specific or nuanced information was missing or underrepresented in the training data, it can impact the accuracy of my responses.

It's important to note that my training data consists of a vast amount of text from diverse sources, but it is not exhaustive or up to date with real-time information. My knowledge is based on the data available up until September 2021, and I may not be aware of recent events or developments.

OpenAI, the organization behind ChatGPT, is continually working to improve the quality and coverage of the training data to address these limitations. They also rely on user feedback and interactions to identify areas for improvement and make necessary updates.

If you encounter any specific inaccuracies or shortcomings, please feel free to point them out. Your feedback is valuable in helping me identify areas that require improvement and refining the training process.

Djoko Luknanto: Are you still using the train data of September 2021?

ChatGPT: As an AI language model, my responses are based on a mixture of licensed data, data created by human trainers, and publicly available data. These sources may contain publicly available information up until September 2021. It's important to note that I don't have direct access to information or knowledge beyond that date.

It's worth mentioning that while I strive to provide accurate and up-to-date information, there could be new developments or events that have occurred since my training data was last updated. Therefore, information that is time-sensitive or subject to change may not always reflect the most current state of affairs.

To stay informed about the latest news and developments, it's advisable to refer to trusted sources or consult with domain-specific experts for the most up-to-date information beyond my knowledge cutoff.

Djoko Luknanto: Why don't you used directly data from internet?

ChatGPT: As an AI language model, I don't have direct access to the internet or the ability to fetch real-time information. My responses are generated based on the knowledge and patterns learned from the training data I was provided, which consists of a large corpus of text from various sources.

There are a few reasons why direct access to the internet is not available. First, it would raise concerns about privacy and security. Allowing unrestricted access to the internet could potentially expose sensitive or private information.

Second, the content on the internet is vast and constantly changing. It would be challenging to maintain real-time access to the internet and ensure the accuracy and reliability of the information provided. The training and validation processes used to create AI models like mine involve carefully curating and filtering data to ensure quality and consistency.

However, while I don't have direct access to the internet, I aim to provide helpful and accurate responses based on the information available to me up until my last training data update in September 2021. I encourage users to verify and cross-reference information from reliable sources and consult subject matter experts for the most up-to-date and reliable information.

Djoko Luknanto: When you will update your training data?

ChatGPT: As an AI language model, I don't have visibility or control over the specific schedule or timeline for updating my training data. The decision to update the training data rests with the team of researchers and engineers at OpenAI who oversee the development and maintenance of the model.

OpenAI periodically updates their models and training data to incorporate new information, improve performance, address limitations, and enhance overall user experience. However, the exact timing and frequency of these updates can vary depending on various factors, including the availability and curation of new data, research advancements, and technical considerations.

It's important to note that while updates may occur, I don't have direct information about when a specific update will happen or what specific changes will be made to the training data. If an update is made, OpenAI generally makes an announcement regarding the improvements and changes introduced.

For the most accurate and up-to-date information, it's always recommended to consult reliable sources and refer to the latest news, research, and domain-specific expertise.

Djoko Luknanto: Right now, I am interesting in comparing between ChatGPT, Microsoft Bing AI Copilot, and Google Bard AI

ChatGPT: ChatGPT, Microsoft Bing AI Copilot, and Google Bard AI are three different AI-based language models developed by different organizations. While I can provide some general information about these models, it's important to note that my knowledge is based on publicly available information up until September 2021, and there might have been updates or new developments since then.

1. ChatGPT (OpenAI): ChatGPT is developed by OpenAI and is based on the GPT (Generative Pre-trained Transformer) architecture. It is designed to engage in conversational interactions and provide responses to a wide range of prompts. It has been trained on a diverse dataset and aims to generate coherent and contextually relevant responses.
2. Microsoft Bing AI Copilot: Microsoft Bing AI Copilot is an AI-powered code completion tool specifically designed to assist developers in writing code. It utilizes machine learning techniques and natural language processing to understand code snippets and provide suggestions or completions based on the context. It is trained on vast amounts of code data and is intended to enhance developer productivity.
3. Google Bard AI: As of my knowledge cutoff in September 2021, Google Bard AI was not a publicly known AI model or product developed by Google. It's possible that there have been developments or announcements since then that I'm not aware of.

It's worth mentioning that each of these models serves different purposes and may have different strengths and limitations based on their specific design, training data, and intended use cases. For a comprehensive and accurate understanding of these models, it's recommended to refer to the respective organizations' official documentation, research papers, or any updates provided by them.